What Generative Adversarial Networks Are Capable of? — A Python Project on MNIST Dataset.
pub.towardsai.net·1d
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Automatic Extraction of Road Networks by using Teacher-Student Adaptive Structural Deep Belief Network and Its Application to Landslide Disaster
arxiv.org·1d
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BitNetMCU with CNN: >99.5% MNIST accuracy on a low-end Microcontroller
cpldcpu.com·2d
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Thick practices for AI tools
lesswrong.com·23h
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OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated Image Detection in the Wild
arxiv.org·7h
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Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals
arxiv.org·1d
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Nested Learning: How Your Neural Network Already Learns at Multiple Timescales
rewire.it·1d
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Dynamic Sparsity: Challenging Common Sparsity Assumptions for Learning World Models in Robotic Reinforcement Learning Benchmarks
arxiv.org·7h
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So-called reasoning models are more efficient but not more capable than regular LLMs, study finds
the-decoder.com·16h
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One Model for All: Universal Pre-training for EEG based Emotion Recognition across Heterogeneous Datasets and Paradigms
arxiv.org·7h
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Analysing Environmental Efficiency in AI for X-Ray Diagnosis
arxiv.org·7h
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